Document Type : Research Paper
Authors
1 Faculty of Engineering Science, College of Engineering, University of Tehran, P.O.Box 11365-4563, Tehran, Iran.
2 Faculty of Engineering Science, College of Engineering, University of Tehran.
Abstract
Fuzzy relational inequalities with fuzzy constraints (FRI-FC) represent a generalized form of fuzzy relational inequalities (FRI), in which fuzzy inequality replaces ordinary inequality in the constraints. With fuzzy constraints, we can obtain optimal points (called super-optima) that provide better solutions than those obtained by addressing similar problems with ordinary inequality constraints. In this paper, a linear objective function optimization problem with respect to a max-product FRI-FC problem is considered. Several optimization problems are equivalent to the primal problem, as it has been proven. The main problem is converted into a more simplified problem by means of some simplification operations based on the algebraic structure of the primary problem and its equivalent forms. As a result of a few mathematical manipulations, the main problem has been transformed into a linear optimization model. A super-optimum (which is better than the ordinary feasible optimal solution) is not only found by applying the proposed linearization method, but the best super-optimum is also identified for the main problem. A comparison is made between the current approach and our previous work, in addition to some well-known heuristic algorithms, by applying them to random test problems of different sizes and seeing how they compare to each other. Results demonstrate that the proposed method produces optimal solutions with admissible infeasibilities, while the linearization algorithm produces better solutions than other heuristic algorithms. In addition, the results demonstrate that heuristic algorithms could not escape from poor solutions in most cases.
Keywords
- Fuzzy relational inequalities
- Fuzzy relational equations Fuzzy constraints
- Product t-norm
- Linear optimization
Main Subjects
Sciences, 234 (2013), 71-79. https://doi.org/10.1016/j.ins.2011.04.042
6(6) (2002), 428-435. https://doi.org/10.1007/s00500-001-0157-3
Soft Computing, 11(1) (2007), 33-40. https://doi.org/10.1007/s00500-006-0050-1
problem, Fuzzy Sets and Systems, 188(1) (2012), 58-67. https://doi.org/10.1016/j.fss.2011.07.014
Springer Dordrecht, 1989. https://doi.org/10.1007/978-94-017-1650-5
Fuzzy Sets and Systems, 188(1) (2012), 68-87. https://doi.org/10.1016/j.fss.2011.09.008
Feasibility of fuzzy solutions and solving approach, Information Sciences, 241 (2013), 12-27. https://doi.org/
10.1016/j.ins.2013.04.004
(1999), 107-113. https://doi.org/10.1016/s0165-0114(97)00184-x
234 (2013), 3-15. https://doi.org/10.1016/j.ins.2011.06.009
76(2) (2001), 60-68. https://doi.org/10.1177/003754970107600201
relational inequalities defined by Frank family of t-norms, International Journal in Foundations of Computer Science
and Technology, 8(3) (2018), 1-20. https://doi.org/10.5121/ijfcst.2018.8301
defined by Dubois-Prade family of t-norms, Information Sciences, 503 (2019), 291-306. https://doi.org/10.
1016/j.ins.2019.06.058
with fuzzy relational equations and max- Lukasiewicz composition, Applied Soft Computing, 69 (2018), 475-492.
https://doi.org/10.1016/j.asoc.2018.04.029
defined with max-strict compositions, Information Sciences, 650 (2023), 119696. https://doi.org/10.1016/
j.ins.2023.119696
bipolar fuzzy relational equalities defined with continuous t-norms, Iranian Journal of Fuzzy Systems, 22(3) (2025),
21-38. https://doi.org/10.22111/ijfs.2025.50433.8905
system of bipolar fuzzy relational equalities defined with continuous Archimedean t-norms, Iranian Journal of Fuzzy
Systems, 22(4) (2025), 137-160. https://doi.org/10.22111/ijfs.2025.50906.8994
with max–min composition, Information Sciences, 178(2) (2008), 501-519. https://doi.org/10.1016/j.ins.
2007.07.022
Systems, 206 (2012), 89-102. https://doi.org/10.1016/j.fss.2012.04.009
defined by Dubois-Prade family of t-norms, Computers and Industrial Engineering, 119 (2018), 167-180. https:
//doi.org/10.1016/j.cie.2018.03.038
fuzzy relational inequalities with fuzzy constraints (FRI-FC), Information Sciences, 418 (2017), 317-345. https:
//doi.org/10.1016/j.ins.2017.07.032
region defined by fuzzy relational equations and Schweizer-Sklar family of T-norms, American Journal of Computation,
Communication and Control, 5(2) (2018), 68-87.
inequality constraints, Information Sciences, 252 (2013), 20-31. https://doi.org/10.1016/j.ins.2011.09.030
many constraints of fuzzy relation inequalities, Fuzzy Optimization and Decision Making, 5(1) (2006), 33-47.
https://doi.org/10.1007/s10700-005-4914-0
Optimization and Decision Making, 1(4) (2002), 347-360. https://doi.org/10.1023/A:1020955112523
Fuzzy Sets and Systems, 16(2) (2010), 285-297. https://doi.org/10.1016/j.fss.2009.03.007
(1996), 355-363. https://doi.org/10.1016/0165-0114(95)00221-9
Neural Networks, 4 (1995), 1942-1948. https://doi.org/10.1007/978-0-387-30164-8_630
T composition, Fuzzy Optimization and Decision Making, 7(2) (2008), 169-214. https://doi.org/10.1007/
s10700-008-9029-y
norm, Soft Computing, 18(7) (2014), 1399-1404. https://doi.org/10.1007/s00500-013-1152-1
file sharing systems, IEEE 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, (2012),
452-456. https://doi.org/10.1109/fskd.2012.6233956
Fuzzy Sets and Systems, 160(16) (2009), 2328-2344. https://doi.org/10.1016/j.fss.2009.01.012
181(14) (2011), 2951-2963. https://doi.org/10.1016/j.ins.2011.03.004
composition, Information Sciences, 360 (2016), 149-162. https://doi.org/10.1016/j.ins.2016.04.041
and Systems, 118(3) (2001), 509-517. https://doi.org/10.1016/s0165-0114(98)00417-5
Sets and Systems, 153(2) (2005), 261-273. https://doi.org/10.1016/j.fss.2005.02.010
10(1-3) (1983), 185-201. https://doi.org/10.1016/s0165-0114(83)80114-6
computing series), Springer-Verlag, 2005. https://doi.org/540-31306-0_2
of fuzzy relation equations, Information Sciences, 178(17) (2008), 3482-3490. https://doi.org/10.1016/j.ins.
2008.04.004
complete Brouwerian lattices form lattices, Fuzzy Sets and Systems, 234 (2014), 34-45. https://doi.org/10.
1016/j.fss.2013.03.017
Fuzzy Automated and Decision Processes, (1977), 221-234. https://doi.org/10.1016/b978-1-4832-1450-4.
50017-1
org/10.1016/0165-0114(78)90033-7
1967. https://doi.org/10.1016/j.ins.2007.12.006
Sciences, 181(4) (2011), 832-841. https://doi.org/10.1016/j.ins.2010.10.024
185(3) (2008), 1155-1173. https://doi.org/10.1016/j.ejor.2006.06.046
complete Brouwerian lattices, Information Sciences, 205 (2012), 86-92. https://doi.org/10.1016/j.ins.2012.
04.002
over complete Brouwerian lattices, Information Sciences, 224 (2013), 143-151. https://doi.org/10.1016/j.ins.
2012.10.038
(2007), 4216-4229. https://doi.org/10.1016/j.ins.2007.02.037
Sets and Systems, 150(1) (2005), 147-162. https://doi.org/10.1016/j.fss.2004.09.010
composition, IEEE Transactions on Fuzzy Systems, 16(1) (2008), 73-84. https://doi.org/10.1109/tfuzz.2007.
902018
fuzzy relational equations with max-Archimedean t-norm composition, Fuzzy Sets and Systems, 159(24) (2008),
3347-3359. https://doi.org/10.1016/j.fss.2008.04.007
(2012), 141-152. https://doi.org/10.1016/j.ins.2011.12.030
addition–min composition, Fuzzy Sets and Systems, 255 (2014), 41-51. https://doi.org/10.1016/j.fss.2014.
04.007
with application in wireless communication, Information Sciences, 358 (2016), 44-55. https://doi.org/10.1016/
j.ins.2016.04.014